Advanced Pattern Mining - Data Mining: Concepts and Techniques - page 314

Pattern: “ f frequent, pattern g ”
context indicators:
“mining,” “constraint,” “Apriori,” “FP-growth,”
“rakesh agrawal,” “jiawei han,” . . .
representative transactions:
1) mining frequent patterns without candidate . . .
2) . . . mining closed frequent graph patterns
semantically similar patterns:
“ f frequent, sequential, pattern g ,” “ f graph, pattern g ”
“ f maximal, pattern g ,” “ f frequent, closed, pattern g ,” . . .

Figure 7.12 Semantic annotation of the pattern “f frequent, pattern g.”

In general, the hidden meaning of a pattern can be inferred from patterns with sim-
ilar meanings, data objects co-occurring with it, and transactions in which the pattern
appears. Annotations with such information are analogous to dictionary entries, which
can be regarded as annotating each term with structured semantic information. Let's
examine an example.

Example 7.15 Semantic annotation of a frequent pattern. Figure 7.12 shows an example of a semantic
annotation for the pattern “f frequent, pattern g.” This dictionary-like annotation pro-
vides semantic information related to “f frequent, pattern g,” consisting of its strongest
context indicators , the most representative data transactions , and the most semantically
similar patterns . This kind of semantic annotation is similar to natural language pro-
cessing. The semantics of a word can be inferred from its context, and words sharing
similar contexts tend to be semantically similar. The context indicators and the repre-
sentative transactions provide a view of the context of the pattern from different angles
to help users understand the pattern. The semantically similar patterns provide a more
direct connection between the pattern and any other patterns already known to the
users.

“ How can we perform automated semantic annotation for a frequent pattern? ” The
key to high-quality semantic annotation of a frequent pattern is the successful context
modeling of the pattern. For context modeling of a pattern, p , consider the following.

A context unit is a basic object in a database, D , that carries semantic information
and co-occurs with at least one frequent pattern, p , in at least one transaction in D .
A context unit can be an item, a pattern, or even a transaction, depending on the
specific task and data.
The context of a pattern , p , is a selected set of weighted context units (referred
to as context indicators ) in the database. It carries semantic information, and
co-occurs with a frequent pattern, p . The context of p can be modeled using a
vector space model, that is, the context of p can be represented as C

. p /Dh w . u 1 / ,